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30 lines
1.0 KiB
30 lines
1.0 KiB
3 years ago
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import pickle
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import numpy as np
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import megengine as mge
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import torch
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import torch.nn.functional as F
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def test_meshgrid():
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# Getting back the megengine objects:
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with open('test_data/meshgrid_np_test.pkl', 'rb') as f:
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rx, dilatex, ry, dilatey, x_grid, y_grid = pickle.load(f)
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x_grid = x_grid.numpy()
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y_grid = y_grid.numpy()
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# Test Pytorch
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x_grid_pytorch, y_grid_pytorch = torch.meshgrid(torch.arange(-rx, rx + 1, dilatex, device='cpu'),
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torch.arange(-ry, ry + 1, dilatey, device='cpu'), indexing='xy')
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error_x = np.mean(x_grid_pytorch.numpy()-x_grid)
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error_y = np.mean(y_grid_pytorch.numpy()-y_grid)
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print(f"test_meshgrid (X) - Avg. Error: {error_x}, \n \
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Obtained shape: {x_grid_pytorch.numpy().shape}, Expected shape: {x_grid.shape}")
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print(f"test_meshgrid (Y) - Avg. Error: {error_y}, \n \
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Obtained shape: {y_grid_pytorch.numpy().shape}, Expected shape: {y_grid.shape}")
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if __name__ == '__main__':
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test_meshgrid()
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